open-jev

open-jev is a browser-focused TypeScript library for typed decisions: one piece of text (the state) plus any number of typed questions go in, and one forward pass returns a calibrated probability distribution per question. Nothing is generated, so an answer is always one of the options you provided.

It runs open reproductions of the shape of TypeSafe AI's Jev "System One" model via @huggingface/transformers (Transformers.js). Everything happens on-device: WebGPU when available, WebAssembly otherwise.

Models

| Alias | Repo | Base | Weights (q4f16 / q4) | Notes | | ---------- | --------------------------------------------------------------------------------------------------------------------- | ---------------- | -------------------- | -------------------------------------------------------------------- | | kev-0.6b | onnx-community/kev-0.6b-ONNX | Qwen3-0.6B-Base | 0.34 GB / 0.38 GB | Default. Small and fast. 8192-token context. | | kev-4b | onnx-community/kev-4b-ONNX | Qwen3-4B-Base | 2.3 GB / 2.5 GB | Most accurate. 8192-token context. Needs a capable GPU. | | open-jev | onnx-community/open-jev-deberta-v3-large-ONNX | DeBERTa-v3-large | 0.35 GB / 0.48 GB | Also ships fp16 (0.88 GB) and fp32 (1.75 GB). 512-token context. |

Pass the alias as model, or any Hugging Face repo id whose config.json carries an open_jev or kev section. The encoding family is detected from that config.

Install

npm install open-jev @huggingface/transformers

@huggingface/transformers is a peer dependency so your app and this library share one copy of Transformers.js and its model cache.

Quick start

import { OpenJev, choice, score, noul } from "open-jev";

const info = await OpenJev.info({ dtype: "q4f16" });
console.log(info.isCached, info.downloadSize, info.device, info.dtype);

const jev = await OpenJev.load({
  model: "kev-0.6b", // default; or "kev-4b", "open-jev"
  dtype: "q4f16",
  onProgress: ({ progress }) =>
    console.log(`Model download: ${Math.round(progress * 100)}%`),
});

const state =
  "I was charged twice for the same order and nobody answers my emails. I want my money back now.";

const [area, sentiment, refund] = await jev.decide(state, [
  choice("Which product area is the message about?", [
    "fees & charges",
    "refund & dispute",
    "card",
    "other",
  ]),
  score("How positive is the sentiment of this message?", [
    "very negative",
    "negative",
    "neutral",
    "positive",
    "very positive",
  ]),
  noul("The customer is asking for a refund."),
]);

area.choice; // "fees & charges" | "refund & dispute" | "card" | "other"
area.confidence; // 0.59
sentiment.level; // "very negative" | ... | "very positive"
sentiment.score; // 0.91 (expected level index, may fall between levels)
refund.answer; // true
refund.probability; // 0.88 (p(yes))

Answers are fully typed: option literals flow from the question into the answer, so area.choice is a union of exactly the strings you passed.

Keyed questions

Pass an object instead of an array and get the answers back under the same keys:

const answers = await jev.decide(state, {
  area: choice("Which product area?", [
    "fees & charges",
    "refund & dispute",
    "other",
  ]),
  refund: noul("The customer is asking for a refund."),
});

answers.area.choice; // "fees & charges" | "refund & dispute" | "other"
answers.refund.answer; // boolean

Question types

| Builder | Type | Answer | | ---------------------------------------------- | -------- | -------------------------------------------------------------- | | choice(instructions, options, descriptions?) | choice | Pick one option. Descriptions render as option: description. | | score(instructions, levels) | score | Rate on an ordered scale of levels (first = lowest). | | noul(statement) | noul | Does the statement hold for the state? (yes/no) |

Limits per model:

| Model | choice options | score levels | | ---------- | ---------------- | -------------- | | open-jev | 2 to 255 | 2 to 10 | | kev-* | 1 to 255 | 2 to 255 |

The builders are optional sugar. Plain objects work too:

await jev.decide(state, [
  {
    type: "choice",
    instructions: "Which team should handle this?",
    options: ["billing", "shipping", "other"],
    descriptions: { billing: "Charges, invoices, payment problems" },
  },
  {
    type: "score",
    instructions: "How urgent?",
    options: ["low", "medium", "high"],
  },
  { type: "noul", instructions: "The customer is angry." },
]);

Answer shapes

type ChoiceAnswer<O> = {
  type: "choice";
  choice: O; // option with the highest probability
  confidence: number; // probability of `choice`
  probabilities: Record<O, number>;
};

type ScoreAnswer<L> = {
  type: "score";
  score: number; // expected level index, 0..levels-1
  normalized: number; // score rescaled to 0..1
  level: L; // level label closest to `score`
  confidence: number; // highest single-level probability
  probabilities: Record<L, number>;
};

type NoulAnswer = {
  type: "noul";
  answer: boolean; // probability >= 0.5
  probability: number; // p(yes)
  confidence: number; // max(p(yes), p(no))
};

API

OpenJev.load(options?): Promise<OpenJev>

Downloads (or reads from cache) the tokenizer and model and resolves to a ready instance. All options are optional:

• model (default "kev-0.6b")
  • "kev-0.6b", "kev-4b", "open-jev", or a Hugging Face repo id / path Transformers.js understands.
• dtype (default "auto")
  • fp32, fp16, q4 or q4f16 (the kev models only ship q4 and q4f16).
  • auto picks the model's best WebGPU variant (q4f16 for kev, fp16 for open-jev) when shader-f16 is supported, q4 everywhere else.
• device (default "auto")
  • webgpu, wasm, or cpu (Node.js).
  • auto picks webgpu when available, cpu in Node.js, otherwise wasm.
• onProgress
  • Called with { progress, loaded, total } while files download. progress is 0..1, loaded and total are bytes. Only fires when the rounded value changes.
• maxLength (default 512 for open-jev, 8192 for kev)
  • Context limit. For open-jev the whole sequence; for kev the state plus one question branch.
• temperature, maxStateTokens, truncation
  • Defaults for decide(), see below.

OpenJev.info(options?): Promise<OpenJevInfo>

Returns model cache/download metadata for a configuration (model, device, dtype) without loading anything.

• isCached: whether every required file is in the browser cache.
• downloadSize: sum of all required file sizes (bytes).
• files: the files Transformers.js will fetch.
• model, family, device, dtype: the resolved runtime.

jev.decide(state, questions, options?)

One forward pass, returns typed answers. Per-call options override the defaults given to load():

• temperature (default: open-jev's calibrated 1.05, 1 for kev)
  • Softmax temperature applied to each question's logits.
• maxStateTokens (default 256 for open-jev, 8192 for kev)
  • Token budget for the state. It is cut further if the questions would not fit in maxLength.
• truncation (default "cut")
  • "cut" drops trailing state tokens, "error" throws when the state does not fit.

decide() throws if the questions alone exceed maxLength. Concurrent calls are queued and run one after another.

jev.countTokens(text): number

Number of tokens text occupies, without markers. Use it to check a state against maxStateTokens up front.

jev.runtime

The { model, family, device, dtype } that were loaded. MODELS maps each alias to its repo id.

jev.dispose(): Promise<void>

Releases the ONNX session. Pending decide() calls finish first; the instance cannot be used afterwards.

How it works

Both families read the state once and score every option of every question in a single pass. The library builds the model-specific sequence and reads the right logits back.

open-jev (DeBERTa-v3-large):

[CLS] [STATE] state [Q] instructions [OPT] option_1 [OPT] option_2 … [Q] … [SEP]

together with a span-slot tensor (seg) and per-pair slot ids (pair_q, pair_opt). The graph returns one logit per (question, option) pair.

kev (Qwen3):

<state> state <q> instructions <opt> option_1 </opt> <opt> option_2 </opt> … <decide> <q> …

The graph takes only input_ids and attention_mask, derives a block-causal mask from the delimiters so each question sees the state and itself only, and returns one logit per token. The library reads the value at every option's </opt> position. Caller text is escaped (<|name|> becomes <¦name¦>) so it can never forge a delimiter.

In both cases a temperature-scaled softmax within each question's group is that question's distribution. noul questions use the fixed options ["no", "yes"] the models were trained with.

Development

pnpm install
pnpm dev

pnpm dev builds the library once, then runs the library watch build and the example app's Vite dev server side by side. Use pnpm dev:lib or pnpm dev:example to run only one of them.

Build for publish:

pnpm build

Type-check:

pnpm typecheck

Publish to npm (checks login, bumps the version, type-checks, builds, publishes):

pnpm release <otp> [patch|minor|major]

Example app

A minimal vanilla demo is included at examples/simple.

cd examples/simple
pnpm install --ignore-workspace
pnpm dev

Notes

• Designed for browser environments; works in Node.js with device: "cpu" (or auto).
• The models are English only. open-jev was trained on three public domains (banking support, movie reviews, Wikipedia yes/no), the kev models on ten. Questions outside these domains work but are less accurate; measure before relying on them. See the model cards linked above for numbers and limitations.
• Loading a kev model logs one Transformers.js warning ("assuming encoder-only architecture"). It is expected: the graph is a custom pointer-head export, not a text generator.
• Model weights are Apache-2.0, this library is MIT.